Probabilistic machine learning: advanced topics

By:

Murphy, Kevin P

Material type: Text

TextSeries: Adaptive Computation and Machine LearningPublication details: The MIT press Cambridge 2023Description: xxxi, 1319 pISBN:

9780262048439

Subject(s):

DDC classification:

006.31015192 MUR

Summary: An advanced book for researchers and graduate students working in machine learning and statistics who want to learn about deep learning, Bayesian inference, generative models, and decision making under uncertainty. An advanced counterpart to Probabilistic Machine Learning: An Introduction, this high-level textbook provides researchers and graduate students detailed coverage of cutting-edge topics in machine learning, including deep generative modeling, graphical models, Bayesian inference, reinforcement learning, and causality. This volume puts deep learning into a larger statistical context and unifies approaches based on deep learning with ones based on probabilistic modeling and inference. With contributions from top scientists and domain experts from places such as Google, DeepMind, Amazon, Purdue University, NYU, and the University of Washington, this rigorous book is essential to understanding the vital issues in machine learning. • Covers generation of high dimensional outputs, such as images, text, and graphs • Discusses methods for discovering insights about data, based on latent variable models • Considers training and testing under different distributions • Explores how to use probabilistic models and inference for causal inference and decision making • Features online Python code accompaniment (https://mitpress.mit.edu/9780262048439/probabilistic-machine-learning/)

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Item type	Current library	Collection	Call number	Copy number	Status	Date due	Barcode
Book	Indian Institute of Management LRC General Stacks	IT & Decisions Sciences	006.31015192 MUR (Browse shelf(Opens below))	1	Available		007056

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An advanced book for researchers and graduate students working in machine learning and statistics who want to learn about deep learning, Bayesian inference, generative models, and decision making under uncertainty.

An advanced counterpart to Probabilistic Machine Learning: An Introduction, this high-level textbook provides researchers and graduate students detailed coverage of cutting-edge topics in machine learning, including deep generative modeling, graphical models, Bayesian inference, reinforcement learning, and causality. This volume puts deep learning into a larger statistical context and unifies approaches based on deep learning with ones based on probabilistic modeling and inference. With contributions from top scientists and domain experts from places such as Google, DeepMind, Amazon, Purdue University, NYU, and the University of Washington, this rigorous book is essential to understanding the vital issues in machine learning.

• Covers generation of high dimensional outputs, such as images, text, and graphs
• Discusses methods for discovering insights about data, based on latent variable models
• Considers training and testing under different distributions
• Explores how to use probabilistic models and inference for causal inference and decision making
• Features online Python code accompaniment
(https://mitpress.mit.edu/9780262048439/probabilistic-machine-learning/)

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